Gm Global Technolgoy Operations, Inc.

Farui Peng

Bakhtiar Brian Litkouhi

Shuqing Zeng

Wei Tong

Shige Wang

Qingrong Zhao

Roy J Mathieu

Hao Yu

Prakash Mohan Peranandam

Arun Adiththan

Wenyuan Qi

Ramesh Sethu

Paolo Giusto

Osman Eser Atesoglu

Chengqi Bian

A system and method of automatic image view alignment for a camera-based road condition detection on a vehicle. The method includes transforming a fisheye image into a non-distorted subject image, comparing the subject image with a reference image, aligning the subject image with the reference image, and analyzing the aligned subject image to detect and identify road conditions in real-time as the vehicle is in operation. The subject image is aligned with the reference image by determining a distance (d) between predetermined feature points of the subject and reference images, estimating a pitchof a projection center based on the distance d, and generating an aligned subject image by applying a rectification transformation on the fisheye image by relocating a center of projection of the fisheye image by the pitch angle.

System and method of automatic image view alignment for camera-based road condition detection on a vehicle

A system for determining calibrated camera extrinsic parameters for an autonomous vehicle includes a camera mounted to the autonomous vehicle collecting image data including a plurality of image frames. The system also includes one or more automated driving controllers in electronic communication with the camera that executes instructions to determine a vehicle pose estimate based on position and movement of the autonomous vehicle by a localization algorithm. The one or more automated driving controllers determine the calibrated camera extrinsic parameters based on three dimensional coordinates for specific feature points of interests corresponding to two sequential image frames, the specific feature points of interests corresponding to the two sequential image frames, and the camera pose corresponding to the two sequential image frames.

System for calibrating extrinsic parameters for a camera in an autonomous vehicle

A computing system including a cooperative system architecture for cataloging, informing, sharing, and managing engineering assets within an organization includes a user management subsystem for creating a registered user profile associated with a specific user of the computing system. The registered user profile includes user metadata that provides identifying characteristics of the specific user. The computing system includes an asset management subsystem including an asset repository for recording one or more engineering assets. The asset management subsystem modifies the user metadata of the registered user profile in response to the specific user recording an engineering asset in the asset repository. The computing system includes a test management subsystem including a test repository for recording one or more test bill of materials.

Cooperative system architecture for engineering asset management and sharing

A method for image style transfer using a Semantic Preserved Generative Adversarial Network (SPGAN) includes: receiving a source image; inputting the source image into the SPGAN; extracting a source-semantic feature data from the source image; generating, by the first decoder, a first synthetic image including the source semantic content of the source image in a target style of a target image using the source-semantic feature data extracted by the first encoder of the first generator network, wherein the first synthetic image includes first-synthetic feature data; determining a first encoder loss using the source-semantic feature data and the first-synthetic feature data; discriminating the first synthetic image against the target image to determine a GAN loss; determining a total loss as a function of the first encoder loss and the first GAN loss; and training the first generator network and the first discriminator network.

Semantic preserved style transfer

A processor-implemented method in a vehicle for performing automatic vehicle lateral control using a neural network for trajectory tracking is provided. The method includes receiving a desired vehicle trajectory; constructing, from the desired trajectory, desired waypoint data for a small number of desired waypoints at different look ahead distances in front of the center of gravity of the vehicle; and generating a steering angle command using a feedforward artificial neural network (ANN) as a function of vehicle speed and the desired waypoint data without modeling vehicle or tire dynamics. The vehicle steering actuator system provides steering control to cause vehicle steering to attempt to achieve the steering angle command.

Trajectory tracking for vehicle lateral control using neural network

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Sterling Heights, MI, United States of America

Farui Peng